| tags: | |
| - CartPole-v1 | |
| - ppo | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - custom-implementation | |
| - deep-rl-class | |
| model-index: | |
| - name: PPO | |
| results: | |
| - metrics: | |
| - type: mean_reward | |
| value: 166.60 +/- 82.10 | |
| name: mean_reward | |
| task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: CartPole-v1 | |
| type: CartPole-v1 | |
| # PPO Agent Playing CartPole-v1 | |
| This is a trained model of a PPO agent playing CartPole-v1. | |
| To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit8 | |
| # Hyperparameters | |
| ```python | |
| {'exp_name': 'ppo' | |
| 'seed': 1 | |
| 'torch_deterministic': True | |
| 'cuda': True | |
| 'track': False | |
| 'wandb_project_name': 'cleanRL' | |
| 'wandb_entity': None | |
| 'capture_video': False | |
| 'env_id': 'CartPole-v1' | |
| 'total_timesteps': 50000 | |
| 'learning_rate': 0.00025 | |
| 'num_envs': 4 | |
| 'num_steps': 128 | |
| 'anneal_lr': True | |
| 'gae': True | |
| 'gamma': 0.99 | |
| 'gae_lambda': 0.95 | |
| 'num_minibatches': 4 | |
| 'update_epochs': 4 | |
| 'norm_adv': True | |
| 'clip_coef': 0.2 | |
| 'clip_vloss': True | |
| 'ent_coef': 0.01 | |
| 'vf_coef': 0.5 | |
| 'max_grad_norm': 0.5 | |
| 'target_kl': None | |
| 'repo_id': 'trtd56/ppo-CartPole' | |
| 'batch_size': 512 | |
| 'minibatch_size': 128} | |
| ``` | |